README_space.md

📊 Seaborn & Matplotlib Visual Lab

Interactive Streamlit app for exploring Seaborn and Matplotlib side by side — from quick EDA plots to code snippets you can reuse in your notebooks.

This Space runs directly in the browser.
No file uploads are required: the app uses Seaborn’s built-in demo datasets for safe, fast experimentation.

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📌 What this app does

The Seaborn & Matplotlib Visual Lab lets you:

• Load classic Seaborn demo datasets (Tips, Penguins, Flights, Iris, Diamonds, Titanic, Car Crashes)
• Build Seaborn plots interactively (distribution, relationships, categories, heatmaps, pairplots)
• Recreate the same ideas using Matplotlib with more low-level control
• Compare Seaborn vs Matplotlib for the same pattern in one screen
• Save plots into a gallery and download them as PNG or a ZIP archive

Use it as a small visual lab for plots: learn, tweak, copy the code, and move it into your own projects.

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📸 Dashboard Preview

1️⃣ Seaborn — Distribution Builder (Tips)

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2️⃣ Seaborn — Relationship Builder (Tips)

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3️⃣ Matplotlib — Histogram (Iris)

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4️⃣ Matplotlib — Line Plot (Iris)

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5️⃣ Compare — Histogram + KDE (Tips)

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6️⃣ Compare — Scatter (Flights)

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🧭 How to use this Space

The app is organised into five main tabs:

1. Overview

High-level view of the active dataset:

• Sample preview (top rows)
• Column types and missingness summary
• Quick numeric distribution
• Small correlation heatmap for a subset of numeric features

Good starting point for any dataset before plotting.

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2. Seaborn builder

UI-driven Seaborn plots:

• Plot families: Distribution, Relationship, Category, Matrix / Heatmap, Multi-variable
• Controls for:
  • Numeric / categorical column selection
  • Bins, KDE, ECDF, log-scale
  • Hue grouping and top-K categories
• Auto-updated Python code snippet that you can copy into a notebook

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3. Matplotlib builder

Low-level Matplotlib plotting:

• Plot types: Line, Scatter, Bar, Histogram, Box, Subplots overview
• Controls for:
  • Axes selection (X/Y)
  • Markers, point size, transparency
  • Horizontal vs vertical bars
  • Density vs counts, optional KDE overlay in overview

The goal is to show how to translate visual ideas into raw Matplotlib commands.

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4. Compare

Side-by-side comparison of Seaborn and Matplotlib for:

• Distribution pattern: histogram + KDE
• Relationship pattern: scatter plot

Useful for teaching how high-level Seaborn APIs map to Matplotlib primitives.

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5. Gallery

A lightweight export hub:

• Save any Seaborn or Matplotlib plot into a session gallery
• Download individual PNGs
• Prepare and download a ZIP with all saved plots

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📚 Data & Datasets

All data lives inside the Space and comes from Seaborn’s built-in datasets.
No uploads, no external APIs, and no personal data.

Available datasets:

• tips
• penguins (NaNs dropped)
• flights
• iris
• diamonds (1,000-row sample)
• titanic
• car_crashes

Switch between them from the sidebar and see the plots update instantly.

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🧩 Tech Stack

• Python
• Streamlit — app framework
• Seaborn — high-level statistical plotting
• Matplotlib — core plotting engine
• NumPy & pandas — data handling

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🖥 Run locally (optional)

If you want to run the same app outside Hugging Face Spaces:

git clone https://github.com/tarekmasryo/seaborn-matplotlib-visual-lab.git
cd seaborn-matplotlib-visual-lab

pip install -r requirements.txt
streamlit run app.py

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Use this Space as a safe place to experiment with plots, learn the APIs, and copy production-ready snippets into your own notebooks and dashboards.